Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from
coordinates (eg, xyz) to signals (eg, signed distances), have shown remarkable promise as …

We present a novel type of neural fields that uses general radial bases for signal
representation. State-of-the-art neural fields typically rely on grid-based representations for …

Neural fields, also known as implicit neural representations, have emerged as a powerful
means to represent complex signals of various modalities. Based on this Dupont et al.(2022) …

Factored feature volumes offer a simple way to build more compact, efficient, and
intepretable neural fields, but also introduce biases that are not necessarily beneficial for …

Abstract Implicit Neural Representations (INR) use multilayer perceptrons to represent high-
frequency functions in low-dimensional problem domains. Recently these representations …

Diffusion models have emerged as the state-of-the-art for image generation, among other
tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural …

Neural implicit fields are powerful for representing 3D scenes and generating high-quality
novel views, but it remains challenging to use such implicit representations for creating a 3D …

Diffusion models have shown great promise for image generation, beating GANs in terms of
generation diversity, with comparable image quality. However, their application to 3D …

Diffusion models have emerged as the best approach for generative modeling of 2D images.
Part of their success is due to the possibility of training them on millions if not billions of …

Automatically generating high-quality real world 3D scenes is of enormous interest for
applications such as virtual reality and robotics simulation. Towards this goal, we introduce …